- AI refers to "Artificial Intelligence" that refers to different systems or machines that impersonate human intelligence to execute tasks and can iteratively enhance themselves based on the information they accumulate. AI is basically implemented in several forms. It is one sort of a technology that has a never-ending demand. And if you also want to establish your career in this field, taking up a professional course will be beneficial for your career.
- And if you have no idea where to start, Croma Campus is the best place for you to know about AI right from the beginning. Yes, it's being considered one of the best Artificial Intelligence Training Institute in Gurgaon, and imbibing information from this institution will be fruitful for your career.
- To understand every bit of Artificial Intelligence in a much better way. You need to get started with its legit Artificial Intelligence Online Training. In this case, approaching Croma Campus will be beneficial for you as you will end up learning everything in quite an explained manner.
- Lets have a look at what you will end up imbibing at the completion of this course.
Basic information regarding Artificial intelligence, and Neural networks.
You will also receive sessions regarding Pattern recognition, Algorithms, Machine Learning, Language processing.
Our qualified trainers will also help you know about Robotics, Automata, Deep learning, Sensors, and Programming language.
Along with imbibing detailed information concerning the main section, and sub-section of this subject, you will also cover some general topics like-Engineering - custom made for CS students, Biology for CS students, Math, Physics - for CS students, Design, and Object-oriented application respectively.
- The salary package in this field is high right from the beginning level. So, if you are worried about the salary package, then you shouldn't stress about it and rather start brushing up your skills to imbibe more skills. A proper certification of Artificial Intelligence Training in Gurgaon will therefore help you in grabbing a higher package.
Well, as a fresher AI Developer, you will earn around Rs. 8 Lakhs per year in the beginning.
Further, you will earn around even higher salary around Rs. 50 Lakhs annually.
Well, later in your career this salary structure will get expanded by imbibing more skills, and the latest information.
Post having this skill, you will be able to enter into a multi-national company that will also offer you a quite higher salary package.
- Well, there are various AI jobs with skilled professionals to fill them, and as we know shortly, the AI world has shown no signs of slowing down, so the demand is genuinely very high. So, if your interest also lies in this field, opting for Artificial Intelligence Training in Gurgaon will be beneficial for your career.
By getting started with Artificial Intelligence Training in Gurgaon, you will turn into a knowledgeable AI Developer.
Enrolling in this course will allow you to examine this subject right from the scratch.
By knowing every side of Artificial Intelligence Training in Gurgaon, you will end up acquiring a higher position.
Your basic AI knowledge will also get strengthened.
You will have numerous international jobs offers in hand as well.
- An AI Developer is supposed to execute a wide series of tasks. So, if you also want to turn into a skilled AI Developer, by getting associated with Artificial Intelligence Training Institute in Gurgaon will be appropriate for your career.
Your first duty will be to solve numerous business challenges using AI software.
You will also have to design, build, implement, and monitor AI systems.
Furthermore, you need to also describe to project managers and stakeholders the potential and limitations of AI systems.
You will also have to develop data ingest and data transformation architecture.
Moreover, you might also have to look out for new AI technologies to imply within the business.
- In the present scenario, you will find numerous reasons to get started with this specific course. Moreover, by obtaining information concerning Artificial Intelligence Training in Gurgaon, you will grow in this field quicker and acquire a higher position as well. By associating with a proper Artificial Intelligence Training Institute in Gurgaon, you will get the chance to know some of the main reasons to learn this course.
One of the main reasons is its hugely increasing demand for skilled professionals in AI Developers.
Moreover, it has a wide bright future ahead.
By knowing this technology, you will be able to integrate complicated tasks without the need of cost delays respectively.
You will get a series of job roles in this field as well.
Immense openings for skilled developers in huge companies.
- Presently, you will find various companies hiring skilled AI Developers. So, if youre aim is to acquire a job post completing the Artificial Intelligence Training in Gurgaon, then you genuinely don't have to worry at all as you will surely end up getting into a well-established as theres genuinely a huge space for skilled candidates, and we will also set your interviews with established companies. So, getting in touch with Artificial Intelligence Training Institute in Gurgaon will only uplift your career graph.
Precily Private Limited, Iora Ecological Solutions, Gauge Data Solutions Pvt Ltd, etc. are some of the well-established companies hiring skilled AI Developers.
Our faculty members will help you in clearing the interview by often conducting a mock test.
The main agenda of Artificial Intelligence Training in Gurgaon is to assist you to get settled in a well-established workplace.
- For the past few years, Croma Campus has been considered the best provider of Artificial Intelligence Training in Gurgaon. It basically aims at producing qualitative training along with enough study material and numerous instances. So, if you are also looking to acquire detailed information concerning Artificial Intelligence, getting associated with Croma Campus will be an ideal move toward your career.
Getting in touch with us will give you ample chances to obtain the latest information concerning the Artificial Intelligence course.
Here, you will obtain information regarding its related course.
Croma Campus will offer you placement assistance.
Well, right from the beginning, our faculty members will give you suggestive tips to clear the interview process.
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CURRICULUM & PROJECTS
Artificial Intelligence Certification training
- Python Statistics for AI
- Python - MySQL
- Data Science Professional Program
- Machine Learning
- Live Projects
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Variables, expression condition and function
- Global and Local Variables in Python
- Packing and Unpacking Arguments
- Type Casting in Python
- Byte objects vs. string in Python
- Variable Scope
- Declaring and usingNumeric data types
- Using stringdata type and string operations
- Understanding Non-numeric data types
- Understanding the concept of Casting and Boolean.
- Strings
- List
- Tuples
- Dictionary
- Sets
- Python Keyword and Identifiers
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- List
- What is List.
- List Creation
- List Length
- List Append
- List Insert
- List Remove
- List Append & Extend using “+” and Keyword
- List Delete
- List related Keyword in Python
- List Revers
- List Sorting
- List having Multiple Reference
- String Split to create a List
- List Indexing
- List Slicing
- List count and Looping
- List Comprehension and Nested Comprehension
- Dictionary
- Dict Creation
- Dict Access (Accessing Dict Values)
- Dict Get Method
- Dict Add or Modify Elements
- Dict Copy
- Dict From Keys.
- Dict Items
- Dict Keys (Updating, Removing and Iterating)
- Dict Values
- Dict Comprehension
- Default Dictionaries
- Ordered Dictionaries
- Looping Dictionaries
- Dict useful methods (Pop, Pop Item, Str , Update etc.)
- Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- Tuple
- What is Tuple
- Tuple Creation
- Accessing Elements in Tuple
- Changinga Tuple
- TupleDeletion
- Tuple Count
- Tuple Index
- TupleMembership
- TupleBuilt in Function (Length, Sort)
- Control Flow
- Loops
- Loops and Control Statements (Continue, Break and Pass).
- Looping techniques in Python
- How to use Range function in Loop
- Programs for printing Patterns in Python
- How to use if and else with Loop
- Use of Switch Function in Loop
- Elegant way of Python Iteration
- Generator in Python
- How to use nested IF and Else in Python
- How to use nested Loop in Python
- Use If and Else in for and While Loop
- Examples of Looping with Break and Continue Statements
- How to use IN or NOTkeywordin Python Loop.
- Python Exception Handling
- Python Errors and Built-in-Exceptions
- Exception handing Try, Except and Finally
- Catching Exceptions in Python
- Catching Specific Exception in Python
- Raising Exception
- Try and Finally
- Python File Handling
- Opening a File
- Python File Modes
- Closing File
- Writing to a File
- Reading from a File
- Renaming and Deleting Files in Python
- Python Directory and File Management
- List Directories and Files
- Making New Directory
- Changing Directory
- Python Function, Modules and Packages
- Python Syntax
- Function Call
- Return Statement
- Write an Empty Function in Python –pass statement.
- Lamda/ Anonymous Function
- *argsand **kwargs
- Help function in Python
- Scope and Life Time of Variable in Python Function
- Nested Loop in Python Function
- Recursive Function and Its Advantage and Disadvantage
- Organizing python codes using functions
- Organizing python projects into modules
- Importing own module as well as external modules
- Understanding Packages
- Programming using functions, modules & external packages
- Map, Filter and Reduce function with Lambda Function
- More example of Python Function
- Python Object Oriented Programming—Oops
- Concept of Class, Object and Instances
- Constructor, Class attributes and Destructors
- Real time use of class in live projects
- Inheritance, Overlapping and Overloading operators
- Adding and retrieving dynamic attributes of classes
- Programming using Oops support
- Python Database Interaction
- SQL Database connection using
- Creating and searching tables
- Reading and Storing configinformation on database
- Programming using database connections
- Reading an excel
- Reading an excel file usingPython
- Writing toan excel sheet using Python
- Python| Reading an excel file
- Python | Writing an excel file
- Adjusting Rows and Column using Python
- ArithmeticOperation in Excel file.
- Plotting Pie Charts
- Plotting Area Charts
- Plotting Bar or Column Charts using Python.
- Plotting Doughnut Chartslusing Python.
- Consolidationof Excel File using Python
- Split of Excel File Using Python.
- Play with Workbook, Sheets and Cells in Excel using Python
- Creating and Removing Sheets
- Formatting the Excel File Data
- More example of Python Function
- Working with PDF and MS Word using Python
- Extracting Text from PDFs
- Creating PDFs
- Copy Pages
- Split PDF
- Combining pages from many PDFs
- Rotating PDF’s Pages
- Complete Understanding of OS Module of Python
- Check Dirs. (exist or not)
- How to split path and extension
- How to get user profile detail
- Get the path of Desktop, Documents, Downloads etc.
- Handle the File System Organization using OS
- How to get any files and folder’s details using OS
- Pandas
- Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
- How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function
- Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
- Exploring the Data Plotting, Correlations, and Histograms
- Getting statistical information about the data Analysis Concepts, Handle the None Values
- Reading files with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
- Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
- Applying formulas on the columns Basic Grouping, Concepts of Aggregate Function
- Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (Setting Indices)
- Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
- Exporting the results to Excel Joins:
- Python | Pandas Data Frame Inner Join
- Under sting the properties of Data Frame Left Join (Left Outer Join)
- Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
- Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
- Pandas | Find Missing Data and Fill and Drop NA Appending DataFrameand Data
- Pandas | How to Group Data How to apply Lambda / Function on Data Frame
- Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
- NumPy
- Introduction to NumPy: Numerical Python
- Importing NumPy and Its Properties
- NumPy Arrays
- Creating an Array from a CSV
- Operations an Array from aCSV
- Operations with NumPy Arrays
- Two-Dimensional Array
- Selecting Elements from 1-D Array
- Selecting Elements from 2-D Array
- Logical Operation with Arrays
- Indexing NumPy elements using conditionals
- NumPy’sMean and Axis
- NumPy’sMode, Median and Sum Function
- NumPy’sSort Function and More
- MatPlotLib
- Bar Chart using Python MatPlotLib
- Column Chart using Python MatPlotLib
- Pie Chart using Python MatPlotLib
- Area Chart using Python MatPlotLib
- Scatter Plot Chart using Python MatPlotLib
- Play with Charts Properties Using MatPlotLib
- Export the Chart as Image
- Understanding plt. subplots () notation
- Legend Alignment of Chart using MatPlotLib
- Create Charts as Image
- Other Useful Properties of Charts.
- Complete Understanding of Histograms
- Plotting Different Charts, Labels, and Labels Alignment etc.
- Introduction to Seaborn
- Introduction to Seaborn
- Making a scatter plot with lists
- Making a count plot with a list
- Using Pandas with seaborn
- Tidy vs Untidy data
- Making a count plot with a Dataframe
- Adding a third variable with hue
- Hue and scattera plots
- Hue and count plots
- Visualizing Two Quantitative Variables
- Introduction to relational plots and subplots
- Creating subplots with col and row
- Customizing scatters plots
- Changing the size of scatter plot points
- Changing the style of scatter plot points
- Introduction to line plots
- Interpreting line plots
- Visualizing standard deviation with line plots
- Plotting subgroups in line plots
- Visualizing a Categorical and a Quantitative Variable
- Current plots and bar plots
- Count plots
- Bar plot with percentages
- Customizing bar plots
- Box plots
- Create and interpret a box plot
- Omitting outliers
- Adjusting the whiskers
- Point plots
- Customizing points plots
- Point plot with subgroups
- Customizing Seaborn Plots
- Changing plot style and colour
- Changing style and palette
- Changing the scale
- Using a custom palette
- Adding titles and labels: Part 1
- Face Grids vs. Axes Subplots
- Adding a title to a face Grid object
- Adding title and labels: Part 2
- Adding a title and axis labels
- Rotating x-tics labels
- Putting it all together
- Box plot with subgroups
- Bar plot with subgroups and subplots
- Introduction to MySQL
- What is the MySQLdb
- How do I Install MySQLdb
- Connecting to the MYSQL
- Selecting a database
- Adding data to a table
- Executing multiple queries
- Exporting and Importing data tables.
- SQL Functions
- Single Row Functions
- Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date
- Count, Average, Sum, Now etc.
- Obtaining data from Multiple Tables
- Types of Joins (Inner Join, Left Join, Right Join & Full Join)
- Sub-Queries Vs. Joins
- Distinct, Order by, Group by, Equal to etc.
- Not Null
- Unique
- Primary Key
- Foreign Key
- SQL Basic
- SQL Introduction
- SQL Syntax
- SQL Select
- SQL Distinct
- SQL Where
- SQL And & Or
- SQL Order By
- SQL Insert
- SQL Update
- SQL Delete
- SQL Advance
- SQL Like
- SQL Wildcards
- SQL In
- SQL Between
- SQL Alias
- SQL Joins
- SQL Inner Join
- SQL Left Join
- SQL Right Join
- SQL Full Join
- SQL Union
- SQL Functions
- SQL Avg()
- SQL Count()
- SQL First()
- SQL Last()
- SQL Max()
- SQL Min()
- SQL Sum()
- SQL Group By
- What is Analytics & Data Science
- Common Terms in Analytics
- What is data
- Classification of data
- Relevance in industry and need of the hour
- Types of problems and business objectives in various industries
- How leading companies are harnessing the power of analytics
- Critical success drivers
- Overview of analytics tools & their popularity
- Analytics Methodology & problem-solving framework
- List of steps in Analytics projects
- Identify the most appropriate solution design for the given problem statement
- Project plan for Analytics project & key milestones based on effort estimates
- Build Resource plan for analytics project
- Why Python for data science
- Importing Data from various sources (Csv, txt, excel, access etc)
- Database Input (Connecting to database)
- Viewing Data objects - sub setting, methods
- Exporting Data to various formats
- Important python modules: Pandas
- Cleansing Data with Python
- Filling missing values using lambda function and concept of Skewness.
- Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
- Normalizing data
- Feature Engineering
- Feature Selection
- Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
- Label encoding/one hot encoding
- Introduction exploratory data analysis
- Descriptive statistics, Frequency Tables and summarization
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)
- Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)
- Descriptive Statistics
- Sample vs Population Statistics
- Random variables
- Probability distribution functions
- Expected value
- Normal distribution
- Gaussian distribution
- Z-score
- Central limit theorem
- Spread and Dispersion
- Inferential Statistics-Sampling
- Hypothesis testing
- Z-stats vs T-stats
- Type 1 & Type 2 error
- Confidence Interval
- ANOVA Test
- Chi Square Test
- T-test 1-Tail 2-Tail Test
- Correlation and Co-variance
- Concept of model in analytics and how it is used
- Common terminology used in Analytics & Modelling process
- Popular Modelling algorithms
- Types of Business problems - Mapping of Techniques
- Different Phases of Predictive Modelling
- Need for structured exploratory data
- EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
- Identify missing data
- Identify outliers’ data
- Imbalanced Data Techniques
- Data Preparation
- Feature Engineering
- Feature Scaling
- Datasets
- Dimensionality Reduction
- Anomaly Detection
- Parameter Estimation
- Data and Knowledge
- Selected Applications in Data Mining
- Artificial Intelligence
- AI overview
- Meaning, scope, and 3 stages of AI
- Decoding AI
- Features of AI
- Applications of AI
- Image recognition
- Effect of AI on society
- AI for industries
- Overview of machine learning
- ML and AI relationship
- Machine Learning
- Techniques of Machine Learning
- Machine Learning Algorithms
- Algorithmic models of Learning
- Applications of Machine Learning
- Large Scale Machine Learning
- Computational Learning theory
- Reinforcement Learning
- Supervised Learning
- What is Supervised Learning
- Algorithms in Supervised learning
- Regression & Classification
- Regression vs classification
- Computation of correlation coefficient and Analysis
- Multivariate Linear Regression Theory
- Coefficient of determination (R2) and Adjusted R2
- Model Misspecifications
- Economic meaning of a Regression Model
- Bivariate Analysis
- Naive Bayes classifier, Model Training
- ANOVA (Analysis of Variance)
- Semi-supervised and Reinforcement Learning
- Bias and variance Trade-off
- Representation Learning
- Regression and its Types
- Logistic Regression
- Linear Regression
- Polynomial Regression
- Meaning and Types of Classification
- Nearest Neighbor Classifiers
- K-nearest Neighbors
- Probability and Bayes Theorem
- Support Vector Machines
- Naive Bayes
- Decision Tree Classifier
- Random Forest Classifier
- About Clustering
- Clustering Algorithms
- K-means Clustering
- Hierarchical Clustering
- Distribution Clustering
- Ensemble approach
- K-fold cross validation
- Grid search cross validation
- Ada boost and XG Boost
- What are the Limitations of Machine Learning
- What is Deep Learning
- Advantage of Deep Learning over Machine learning
- Reasons to go for Deep Learning
- Real-Life use cases of Deep Learning
- What is Deep Learning Networks
- Why Deep Learning Networks
- How Deep Learning Works
- Feature Extraction
- Working of Deep Network
- Training using Backpropagation
- Variants of Gradient Descent
- Types of Deep Networks
- Feed forward neural networks (FNN)
- Convolutional neural networks (CNN)
- Recurrent Neural networks (RNN)
- Generative Adversal Neural Networks (GAN)
- Restrict Boltzman Machine (RBM)
- Define Keras
- How to compose Models in Keras
- Sequential Composition
- Functional Composition
- Predefined Neural Network Layers
- What is Batch Normalization
- Saving and Loading a model with Keras
- Customizing the Training Process
- Intuitively building networks with Keras
- Introduction to Convolutional Neural Networks
- CNN Applications
- Architecture of a Convolutional Neural Network
- Convolution and Pooling layers in a CNN
- Understanding and Visualizing CNN
- Transfer Learning and Fine-tuning Convolutional Neural Networks
- Intro to RNN Model
- Application use cases of RNN
- Modelling sequences
- Training RNNs with Backpropagation
- Long Short-Term Memory (LSTM)
- Recursive Neural Tensor Network Theory
- Recurrent Neural Network Model
- Time Series Forecasting
- NLP with python
- Bags of words
- Stemming
- Tokenization
- Lemmatization
- TF-IDF
- Sentiment Analysis
- Overview of Tensor Flow
- Tensor Flow code-basics
- Graph Visualization
- Constants, Placeholders, Variables
- Tensor flow Basic Operations
- Linear Regression with Tensor Flow
- Logistic Regression with Tensor Flow
- K Nearest Neighbor algorithm with Tensor Flow
- K-Means classifier with Tensor Flow
- Random Forest classifier with Tensor Flow
- Quick recap of Neural Networks
- Activation Functions, hidden layers, hidden units
- Illustrate & Training a Perceptron
- Important Parameters of Perceptron
- Understand limitations of A Single Layer Perceptron
- Illustrate Multi-Layer Perceptron
- Back-propagation – Learning Algorithm
- Understand Back-propagation – Using Neural Network Example
- TensorBoard
- What is Big Data
- Big Data Customer Scenarios
- Understanding BIG Data: Summary
- Few Examples of BIG Data
- Why BIG data is a BUZZ
- How Hadoop Solves the Big Data Problem
- What is Hadoop
- Hadoop’s Key Characteristics
- Hadoop Cluster and its Architecture
- Hadoop: Different Cluster Modes
- Why Spark is needed
- What is Spark
- How Spark differs from other frameworks
- Spark at Yahoo!
- What is BIG data Analytics
- Why BIG Data Analytics is a ‘need’ now
- BIG Data: The Solution
- Implementing BIG Data Analytics – Different Approaches
- The Traditional Approach: Business Requirement Drives Solution Design
- The BIG Data Approach: Information Sources drive Creative Discovery
- Traditional and BIG Data Approaches
- BIG Data Complements Traditional Enterprise Data Warehouse
- Traditional Analytics Platform v/s BIG Data Analytics Platform
- Scala
- What is Scala
- Scala in other Frameworks
- Introduction to Scala REPL
- Basic Scala Operations
- Variable Types in Scala
- Control Structures in Scala
- Understanding the constructor overloading,
- Various abstract classes
- The hierarchy types in Scala,
- For-each loop, Functions and Procedures
- Collections in Scala- Array
- Spark
- Overview to Spark
- Spark installation, Spark configuration,
- Spark Components & its Architecture
- Spark Deployment Modes
- Limitations of Map Reduce in Hadoop
- Working with RDDs in Spark
- Introduction to Spark Shell
- Deploying Spark without Hadoop
- Parallel Processing
- Spark MLLib - Modelling Big Data with Spark
- What is Kafka Why Kafka
- Configuring Kafka Cluster
- Kafka architecture
- Producing and consuming messages
- Operations, Kafka monitoring tool
- Need of Apache Flume
- What is Apache Flume
- Understanding the architecture of Flume
- Basic Flume Architecture
- Managing credit card Risks
- Bank Loan default classification
- YouTube Viewers prediction
- Super store Analytics (E-commerce)
- Buying and selling cars prediction (like OLX process)
- Advanced House price prediction
- Analytics on HR decisions
- Survival of the fittest
- Twitter Analysis
- Flight price prediction
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Croma Campus is the well-known Artificial Intelligence Training in Gurgaon with high tech infrastructure and lab facilities. We also provide online access to servers so that candidates will implement the projects at their homes easily. Croma Campus in Gurgaon mentored more than 3000+ candidates with Artificial Intelligence Training Institute in Gurgaon at a very reasonable fee. The course curriculum is customized as per the requirement of candidates/corporates.
Artificial Intelligence (AI) assistance for the job - Like Data Scientists, Software Analysts and Developers, Computer Engineers and Computer Scientists, Algorithm Specialists, Research Scientists and Engineering consultants, Maintenance Technicians, and Mechanical Engineers.
Croma Campus: Artificial Intelligence Training Institute in Gurgaon conducted by specialist working certified corporate professionals having 8+ years of experience in implementing real-time Artificial Intelligence projects. Candidates will implement the following concepts under Artificial Intelligence: Fundamentals of Reinforcement Learning, Q-Learning Intuition, Building a Self-Driving Car, Creating the environment, Building an AI on real-time projects along with Artificial Intelligence Placement Training modules like aptitude test preparation, etc.
There are four types of Artificial Intelligence: reactive machines, limited memory, the theory of mind and self-awareness.
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- Email: info@cromacampus.com
- Address: G-21, Sector-03, Noida (201301)

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